In the past ten years, the booming technology industry has promoted the economy of the Bay Area, the real estate crisis has intensified, and long-term demographic trends have become the forefront. According to the latest data from ACS (https://www.mercurynews.com/2020/01/09/how-bay-area-demographics-have-changed-since-2010/), starting in 2012, the population growth rate of the Bay Area peaked and then plummeted. The current population growth rate has reached the lowest point in history in 14 years. At the same time, income levels and housing prices in the Bay Area have increased, although different regions have different rates of increase. Major changes in the demographic structure continue, and the trend of population aging continues. In this context, we began to consider what are the internal and external population change trends in various counties in the Bay Area? How much migration specifically within Bay Area counties to each other? In the past 10 years, how has the demographic compositions of the Bay Area changed? Do these demographic compositions affect population change or migration? In this project, we will consider demographic compositions such as housing, education, housing, poverty and employment.

part1 internal and external population change in the Bay Area

population in county level, from 2018 to 2019

From 2010 to 2019, the internal net population change of almost all counties in the Bay Area was fluctuating (especially in Santa Clara). In the first few years of the 2010s, internal net population change in most counties in the Bay Area are positive, while this fluctuation is more biased towards negative internal net population change in recent years. Except for 2012, San Francisco has continued to have positive internal net population change from 2010 to 2019. Alameda also showed the same positive internal net population change (except for 2016).

From 2012 to 2016, almost all counties in the Bay Area had positive external net flow, while in the first and last few years of 2010s, the external net flow differences among the counties in the Bay Area was greater. In addition, during this decade (from 2010 to 2019), the Southeastern part of the Bay Area has had a higher external net population growth than the Northwestern part of the Bay Area.

See internal and external net population change from 2010 to 2019: https://hhyj4495.shinyapps.io/dashboard_external_internal/

The following will take year 2019 as an example to show the internal net flow and external net flow of the counties in the Bay Area.

According to the above chart, in 2019, Alameda and San Francisco has the most positive internal net flow, while Contra Costa and Santa Clara has the most negative internal net flow.

As is shown in this map, Contra Costa and Santa Clara has the most positive external net flow, and San Fracisco and San Mateo has the most negative external net flow.

part2 Demographic compositions changes

Do these demographic compositions affect population change or migration?

In order to analyze the factors that affect migration and population change, I refer to https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5019337/, in which the Area Deprivation Inex (ADI) is explained.

ADI measures the deprivation level of society from four dimensions. The four dimensions are: poverty, housing, employment, education. These dimensions may have a very close relationship with population change and migration. In order to simplify the analysis, I chose census dataset group B17010, which provides information about population below poverty level to represent “poverty”. For housing, I chose B25003 to get number of owner/renter cccupied housing units; for employment, B18120 gives information about unemployed people in the labor force; for education, B15003 provides data of population aged 25+ with at least a high school education.

From 2010 to 2019, the population below the poverty level in each county is in a fluctuating downward trend. Among them, Alameda and Santa Clara have the largest decline (both percentage and absolute value) in the population below the poverty level.

From 2010 to 2019, the number of renter occupied unites in all counties in the Bay Area fluctuated, and there was no significant increase or decrease.

In the last decades, the Bay Area’s unemployed people jas continued to decline. Santa Clara, Alameda had the largest decline rate. While Marin and Napa had the least decline rate. The number of unemployed people in other counties has relatively similar trends.

Next, let’s see are there any changes in educational attainment from 2010 to 2019?

Santa Clara has the largest increase (both absolute value and percentage) in population with a bachelor’s degree or higher, while several counties such as Napa, Sonoma, and Marin have very low population growth rates with a bachelor’s degree or higher. Nevertheless, from 2010 to 2019, all counties in the Bay Area had a increase in educational attainment.

part3 Equity analysis

Visualization and quantification of disproportionate population flow across different income groups

Next, we will analyze the relationship between income and population flow. Do high-income groups and low-income groups have different population flow trends? We classify “no income”,“$1 to $9,999 or loss”,“$10,000 to $14,999”,“$15,000 to $24,999”,“$25,000 to $34,999” as “low income” groups; “$35,000 to $49,999” The group of “,”$50,000 to $64,999" are classified as “median income” groupl; “$65,000 to $74,999”, “$75,000 or more” are classified as the “high income” group. Population flow are classified into three groups: “here since last year”, “outflow”, and “inflow”, and I used the data of 2019 to draw the following figure.

As can be seen, the majority of people have stayed here since last year(year 2018). and the inflow population is slightly more than the outflow population.

For the outflow population, the proportion of low income groups is slightly higher, while the proportion of high income groups is lower. For the inflow population, this distribution is just the opposite. This may be caused by the high cost of living (e.g. housing price) in the Bay Area.

part4 flow to where? flow from where?

How much migration specifically within Bay Area counties?

In this part, dataset “acs/flows” is used. This dataset provides information about the actual county-to-county origin-destinations. Let’s see how much migration specifically within Bay Area counties to each other.

A brief introduction of the data set: API: https://api.census.gov/data/2019/acs/flows/variables.html; county: Combined codes for the reference geography; GEOID2:Combined codes for the second geography; MOVEDIN Total inbound migration to reference geography from second geography; MOVIEDOUT: Total outbound migration.

Becasue San Francisco and San Mateo has the most negative external net population flow, I’ll take these two counties as example to see where do these population flow to in 2019.

See more counties’ migration from 2010 to 2019 in the Bay Area (flow to where?): https://heyaojinghuang.shinyapps.io/dashboard_flow_to_where/

As is shown in the above map, people tends to move to adjacent counties. Also, for a specific county, residents of that county tend to flow out to several fixed counties. Take San Francisco as an example. In 2010, the population of San Francisco tended to flow out to Marin, Contra Costa, Alameda, and Santa Clara. In the following years, the population of San Francisco showed the same preference.

Next, let’s see where do the new population of Santa Clara and Contra Costa come from in 2019.

People tends to move from there original counties to adjacent counties. Santa Clara’s new population most came from Contra Costa, San Mateo and Alameda; Contra Costa’s new population most came from Alameda, Solano and San Francisco.

See more counties’ migration from 2010 to 2019 in the Bay Area (flow from where?): https://heyaojinghuang.shinyapps.io/dashboard_flow_from_where/

part5 matching counties

Match counties according to demographic compositions

Do counties with the similar demographic compositions have similar population flow or migration trends? In this part, I’ll match counties according to the following characteristics: percentage of unemployed people, percentage of people below poverty level, percentage of people with bachelor’s degree or higher, percentage of renter occupied units.

I found that counties with similar demographic compositions have more similar external net flow. This may be due to percentage of unemployed people, percentage of people below poverty level, percentage of people with bachelor’s degree or higher and percentage of renter occupied units are more likely to be changed rapidly due to population inflow and outflow within a short period of time. The internal net flow is mainly determined by the birth rate and death rate. The birth and death can only have a significant impact on the demographic compositions within a relatively long period of time. In addition, I found that in the past 10 years, the population of the Bay Area tends to migrate to its adjacent counties. The impact of demographic compositions on migration is not as significant as that caused by geographic distance